Keywords
Deep learning
Machine learning
E-commerce
Review spam detection
How to Cite
Abstract
Purpose: This study presents a comprehensive survey of methodologies and datasets used for fake review detection in e-commerce platforms. As online reviews increasingly influence consumer purchasing behavior, the proliferation of deceptive content poses serious threats to trust and market integrity.
Design/Methodology/Approach: The paper systematically reviews machine learning and deep learning techniques for detecting fake reviews. It categorizes major approaches—supervised, unsupervised, and semi-supervised learning—highlighting feature extraction strategies (linguistic, behavioral, and network-based) and evaluating benchmark datasets such as Deceptive Opinion Spam Corpus, Amazon, and Yelp. The analysis compares conventional algorithms (Naïve Bayes, Random Forest, SVM) with modern deep learning models (LSTM, CNN, BERT, RoBERTa, and GNNs).
Findings: Results demonstrate that deep learning models outperform traditional machine learning techniques in accuracy, scalability, and contextual understanding. Hybrid and ensemble approaches integrating multiple classifiers enhance precision, recall, and robustness. Despite significant progress, challenges persist, including high similarity between fake and genuine reviews, data scarcity, domain transferability, and evolving spammer tactics.
Research Limitations/Implications: Deep learning models, while powerful, require large annotated datasets and significant computational resources. The study emphasizes the need for explainable AI and privacy-preserving models to strengthen consumer trust and platform integrity.
Originality/Value: This survey provides an up-to-date synthesis of fake review detection research, emphasizing the transition toward deep learning and hybrid models. It contributes to future research directions aimed at developing real-time, cross-domain, and transparent detection systems for sustainable digital ecosystems.
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